Breaking change: change format of instances to be unified with G-KOMET; refactor code

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "KOMET 1.0 is a hand-annotated corpus for metaphorical expressions which contains about 200,000 words from \nSlovene journalistic, fiction and on-line texts. \n\nTo annotate metaphors in the corpus an adapted and modified procedure of the MIPVU protocol \n(Steen et al., 2010: A method for linguistic metaphor identification: From MIP to MIPVU, https://www.benjamins.com/catalog/celcr.14) \nwas used. The lexical units (words) whose contextual meanings are opposed to their basic meanings are considered \nmetaphor-related words. The basic and contextual meaning for each word in the corpus was identified using the \nDictionary of the standard Slovene Language. The corpus was annotated for the metaphoric following relations: \nindirect metaphor (MRWi), direct metaphor (MRWd), borderline case (WIDLI) and metaphor signal (MFlag). \nIn addition, the corpus introduces a new 'frame' tag, which gives information about the concept to which it refers.\n", "citation": "@InProceedings{antloga2020komet,\ntitle = {Korpus metafor KOMET 1.0},\nauthor={Antloga, \u000b{S}pela},\nbooktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student abstracts)},\nyear={2020},\npages={167-170}\n}\n", "homepage": "http://hdl.handle.net/11356/1293", "license": "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"document_name": {"dtype": "string", "id": null, "_type": "Value"}, "idx": {"dtype": "uint32", "id": null, "_type": "Value"}, "idx_paragraph": {"dtype": "uint32", "id": null, "_type": "Value"}, "idx_sentence": {"dtype": "uint32", "id": null, "_type": "Value"}, "sentence_words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "met_type": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "met_frame": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "komet", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5739906, "num_examples": 13963, "dataset_name": "komet"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1293/komet.tei.zip": {"num_bytes": 7311643, "checksum": "213f8f5c5b4e4989705a88e014c345fa6038f66e14a83fecb94e08e9f0da6640"}}, "download_size": 7311643, "post_processing_size": null, "dataset_size": 5739906, "size_in_bytes": 13051549}}
+{"default": {"description": "KOMET 1.0 is a hand-annotated corpus for metaphorical expressions which contains about 200,000 words from \nSlovene journalistic, fiction and on-line texts. \n\nTo annotate metaphors in the corpus an adapted and modified procedure of the MIPVU protocol \n(Steen et al., 2010: A method for linguistic metaphor identification: From MIP to MIPVU, https://www.benjamins.com/catalog/celcr.14) \nwas used. The lexical units (words) whose contextual meanings are opposed to their basic meanings are considered \nmetaphor-related words. The basic and contextual meaning for each word in the corpus was identified using the \nDictionary of the standard Slovene Language. The corpus was annotated for the metaphoric following relations: \nindirect metaphor (MRWi), direct metaphor (MRWd), borderline case (WIDLI) and metaphor signal (MFlag). \nIn addition, the corpus introduces a new 'frame' tag, which gives information about the concept to which it refers.\n", "citation": "@InProceedings{antloga2020komet,\ntitle = {Korpus metafor KOMET 1.0},\nauthor={Antloga, \u000b{S}pela},\nbooktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student abstracts)},\nyear={2020},\npages={167-170}\n}\n", "homepage": "http://hdl.handle.net/11356/1293", "license": "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"document_name": {"dtype": "string", "id": null, "_type": "Value"}, "idx": {"dtype": "uint32", "id": null, "_type": "Value"}, "idx_paragraph": {"dtype": "uint32", "id": null, "_type": "Value"}, "idx_sentence": {"dtype": "uint32", "id": null, "_type": "Value"}, "sentence_words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "met_type": [{"type": {"dtype": "string", "id": null, "_type": "Value"}, "word_indices": {"feature": {"dtype": "uint32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}], "met_frame": [{"type": {"dtype": "string", "id": null, "_type": "Value"}, "word_indices": {"feature": {"dtype": "uint32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "komet", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3481821, "num_examples": 13963, "dataset_name": "komet"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1293/komet.tei.zip": {"num_bytes": 7311643, "checksum": "213f8f5c5b4e4989705a88e014c345fa6038f66e14a83fecb94e08e9f0da6640"}}, "download_size": 7311643, "post_processing_size": null, "dataset_size": 3481821, "size_in_bytes": 10793464}}

komet.py

@@ -3,7 +3,7 @@
 import os
 import re
 import xml.etree.ElementTree as ET
-from typing import List
+from typing import List, Tuple
 
 import datasets
 
@@ -40,44 +40,33 @@ _URLS = {
 }
 
 
+XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}"
+EL_LEAF, EL_TYPE, EL_FRAME = range(3)
+
+
 def namespace(element):
     # https://stackoverflow.com/a/12946675
     m = re.match(r'\{.*\}', element.tag)
     return m.group(0) if m else ''
 
 
-def resolve(element) -> List:
-    def _resolve_recursively(element, metaphor_type: str, frame_buffer: List):
+def word_info(sent_el):
+    def _resolve_recursively(element) -> List:
+        """ Knowingly ignored tags: name (anonymized, without IDs), gap, vocal, pause, del,
+        linkGrp (syntactic dependencies) """
         # Leaf node: word or punctuation character
         if element.tag.endswith(("w", "pc")):
-            if len(frame_buffer) == 0:
-                return element.text, metaphor_type, "O"
-            else:
-                # Frame annotations may be nested, encode them with a "/" separator;
-                # e.g., the first annotation is the frame of the phrase involving current word and the last annotation
-                #  is the frame of a phrase part
-                return element.text, metaphor_type, "/".join(frame_buffer)
+            id_curr = element.attrib[f"{XML_NAMESPACE}id"]
+            return [(id_curr, element.text)]
 
-        # Annotated word or word group
+        # Annotated word or word group - not interested in the annotations in this function
         elif element.tag.endswith("seg"):
-            mtype, new_frame_buffer = "O", list(frame_buffer)
-            if element.attrib["subtype"] != "frame":
-                mtype = element.attrib["subtype"]
-            else:
-                # Frame annotations in KOMET are prepended with "#met.", while those in GKomet are not: unify
-                if element.attrib["ana"].startswith("#met."):
-                    _mframe = element.attrib["ana"][5:]
-                else:
-                    _mframe = element.attrib["ana"]
-                new_frame_buffer.append(_mframe)
-
             parsed_data = []
             for child in element:
-                # spaces between words, skip
-                if child.tag.endswith("c"):
+                if child.tag.endswith("c"):  # empty space betw. words
                     continue
 
-                res = _resolve_recursively(child, mtype, new_frame_buffer)
+                res = _resolve_recursively(child)
                 if isinstance(res, list):
                     parsed_data.extend(res)
                 else:
@@ -85,11 +74,62 @@ def resolve(element) -> List:
 
             return parsed_data
 
-    curr_annotations = _resolve_recursively(element, "O", [])
-    if not isinstance(curr_annotations, list):
-        curr_annotations = [curr_annotations]
+    id_words, words = [], []
+    for child_el in sent_el:
+        curr_annotations = _resolve_recursively(child_el)
+        if curr_annotations is not None:  # None = unrecognized ("unimportant") element
+            for ann in curr_annotations:
+                id_words.append(ann[0])
+                words.append(ann[1])
 
-    return curr_annotations
+    return id_words, words
+
+
+def seg_info(sent_el):
+    def _resolve_recursively(element) -> Tuple:
+        """ Returns (type[, subtype], deeper_elements, latest_element)"""
+        # Leaf node: word or punctuation character
+        if element.tag.endswith(("w", "pc")):
+            id_curr = element.attrib[f"{XML_NAMESPACE}id"]
+            return EL_LEAF, [], [id_curr]
+
+        # Annotated word or word group
+        elif element.tag.endswith("seg"):
+            if element.attrib["subtype"] == "frame":
+                ann_type, subtype = EL_FRAME, element.attrib["ana"]
+                if subtype.startswith("#met."):  # for consistency with G-Komet, remove "#met." prefix from frames
+                    subtype = subtype[5:]
+            elif element.attrib["type"] == "metaphor":
+                ann_type = EL_TYPE
+                subtype = element.attrib["subtype"]
+            else:
+                raise ValueError(f"Unrecognized seg type: {element.attrib['type']}")
+
+            deeper_elements = []
+            latest_element = []
+            for child in element:
+                if child.tag.endswith(("c", "vocal", "pause")):  # empty space betw. words or "special" word
+                    continue
+
+                res = _resolve_recursively(child)
+                if res[0] == EL_LEAF:
+                    latest_element.extend(res[2])
+                else:
+                    deeper_elements.append(res)
+                    latest_element.extend(res[3])
+
+            return ann_type, subtype, deeper_elements, latest_element
+
+    annotations = []
+    for child_el in sent_el:
+        if not child_el.tag.endswith("seg"):
+            continue
+
+        ann_type, subtype, deeper_elements, latest_element = _resolve_recursively(child_el)
+        annotations.extend(list(map(lambda _tup: (_tup[0], _tup[1], _tup[3]), deeper_elements)))
+        annotations.append((ann_type, subtype, latest_element))
+
+    return annotations
 
 
 class Komet(datasets.GeneratorBasedBuilder):
@@ -105,8 +145,14 @@ class Komet(datasets.GeneratorBasedBuilder):
                 "idx_paragraph": datasets.Value("uint32"),
                 "idx_sentence": datasets.Value("uint32"),  # index inside current paragraph
                 "sentence_words": datasets.Sequence(datasets.Value("string")),
-                "met_type": datasets.Sequence(datasets.Value("string")),
-                "met_frame": datasets.Sequence(datasets.Value("string"))
+                "met_type": [{
+                    "type": datasets.Value("string"),
+                    "word_indices": datasets.Sequence(datasets.Value("uint32"))
+                }],
+                "met_frame": [{
+                    "type": datasets.Value("string"),
+                    "word_indices": datasets.Sequence(datasets.Value("uint32"))
+                }]
             }
         )
         return datasets.DatasetInfo(
@@ -133,6 +179,7 @@ class Komet(datasets.GeneratorBasedBuilder):
             curr_path = os.path.join(data_dir, fname)
             if os.path.isfile(curr_path) and fname.endswith(".xml") and fname != "komet.xml":  # komet.xml = meta-file
                 data_files.append(fname)
+        data_files = sorted(data_files)
 
         idx_example = 0
         for fname in data_files:
@@ -143,25 +190,54 @@ class Komet(datasets.GeneratorBasedBuilder):
             NAMESPACE = namespace(root)
 
             idx_sent_glob = 0
-            for idx_par, curr_par in enumerate(root.iterfind(f"{NAMESPACE}p")):
+            for idx_par, curr_par in enumerate(root.iterfind(f".//{NAMESPACE}p")):
+                id2position = {}  # {<idx_sent> -> {<id_word>: <position> foreach word} foreach sent}
+                all_words = []
+
+                # Pass#1: extract word information
+                for idx_sent, curr_sent in enumerate(curr_par.iterfind(f"{NAMESPACE}s")):
+                    id_words, words = word_info(curr_sent)
+
+                    id2position[idx_sent] = dict(zip(id_words, range(len(words))))
+                    all_words.append(words)
+
+                all_types, all_frames = [], []
+
+                # Pass#2: extract annotations from <seg>ments
                 for idx_sent, curr_sent in enumerate(curr_par.iterfind(f"{NAMESPACE}s")):
-                    words, types, frames = [], [], []
-                    for curr_el in curr_sent:
-                        if curr_el.tag.endswith(("w", "pc", "seg")):
-                            curr_res = resolve(curr_el)
-                            for _el in curr_res:
-                                words.append(_el[0])
-                                types.append(_el[1])
-                                frames.append(_el[2])
+                    annotated_segs = seg_info(curr_sent)
+                    all_types.append([])
+                    all_frames.append([])
+
+                    for curr_ann in annotated_segs:
+                        ann_type, ann_subtype, words_involved = curr_ann
+                        if ann_type == EL_TYPE:
+                            all_types[idx_sent].append({
+                                "type": ann_subtype,
+                                "word_indices": [id2position[idx_sent][_id_word] for _id_word in words_involved
+                                                 if _id_word in id2position[idx_sent]]
+                            })
+                        elif ann_type == EL_FRAME:
+                            all_frames[idx_sent].append({
+                                "type": ann_subtype,
+                                "word_indices": [id2position[idx_sent][_id_word] for _id_word in words_involved
+                                                 if _id_word in id2position[idx_sent]]
+                            })
+
+                idx_sent = 0
+                for curr_words, curr_types, curr_frames in zip(all_words, all_types, all_frames):
+                    if len(curr_words) == 0:
+                        continue
 
                     yield idx_example, {
                         "document_name": fname,
                         "idx": idx_sent_glob,
                         "idx_paragraph": idx_par,
                         "idx_sentence": idx_sent,
-                        "sentence_words": words,
-                        "met_type": types,
-                        "met_frame": frames
+                        "sentence_words": curr_words,
+                        "met_type": curr_types,
+                        "met_frame": curr_frames
                     }
                     idx_example += 1
+                    idx_sent += 1
                     idx_sent_glob += 1